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Record W4390357522 · doi:10.1109/lmwt.2023.3343847

Novel FEM-Based Simulation-Inserted Optimization Algorithm Using Improved Complex Newton’s Method for EM Design

2023· article· en· W4390357522 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Microwave and Wireless Technology Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsCarleton University
FundersNational Natural Science Foundation of China
KeywordsDimension (graph theory)Finite element methodComputer scienceMatrix (chemical analysis)AlgorithmSensitivity (control systems)Optimization algorithmMathematical optimizationMathematicsEngineeringElectronic engineeringMaterials science

Abstract

fetched live from OpenAlex

Simulation-inserted optimization (SIO) technology, a novel electromagnetic (EM) optimization technology, uses real-equivalent calculation to solve complex finite element equations. However, the application of the existing SIO method with incremental matrix memory is severely limited to EM optimization problems of smaller dimensionalities. Thus, this letter proposes an improved complex Newton’s simulation-inserted optimization (CNSIO) method for EM design. In the proposed CNSIO algorithm, new formulations based on complex domains are derived to reduce the dimension size of the finite element method (FEM) system matrix and the sensitivity matrices. The proposed CNSIO algorithm enables a faster optimization and requires less memory than that using the basic version of the SIO algorithm. The novel CNSIO algorithm is verified through two applications of EM-based microwave components.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.267
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.269
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it